12. Garland, J., & Bradley, E. (2015). Prediction in projection. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(12), 123108.
13. Kantz, H., & Schreiber, T. (2004). Nonlinear time series analysis(Vol. 7). Cambridge university press.
14. Kennel, M. B., Brown, R., & Abarbanel, H. D. (1992). Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical review A, 45(6), 3403.
15. Kibriya, A. M., & Frank, E. (2007, September). An empirical comparison of exact nearest neighbour algorithms. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 140-151). Springer, Berlin, Heidelberg.
16. Kodba, S., Perc, M., & Marhl, M. (2004). Detecting chaos from a time series. European journal of physics, 26(1), 205.
17. Kumar, N., Zhang, L., & Nayar, S. (2008, October). What is a good nearest neighbors algorithm for finding similar patches in images?. In European conference on computer vision (pp. 364-378). Springer, Berlin, Heidelberg.
18. Lorenz, E. N. (1969). Atmospheric predictability as revealed by naturally occurring analogues. Journal of the Atmospheric sciences, 26(4), 636-646.
19. Moore, P. J., Little, M. A (2014). Enhancements to a method of analogues forecasting algorithm. Nonlinear Theory and Its Applications, IEICE, 6(1), 2-4.
20. Packard, N. H., Crutchfield, J. P., Farmer, J. D., & Shaw, R. S. (1980). Geometry from a time series. Physical review letters, 45(9), 712.
21. Shannon, C. E., & Weaver, W. (1949). The mathematical theory of information.
22. Smola, A. J., & Schцlkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199-222.
23. Takens, F. (1981). Detecting strange attractors in turbulence. In Dynamical systems and turbulence, Warwick 1980 (pp. 366-381). Springer, Berlin, Heidelberg.
24. Vapnik, V. (1995). The nature of statistical learning theory. Springer science & business media.
25. Whitney, H. (1936). Differentiable manifolds. Annals of Mathematics, 645-680.